Search Results (27,257)

Cybersecurity threats are becoming increasingly sophisticated, frequent, and diverse, posing a major risk to critical infrastructure, public trust, and digital economies. Traditional intrusion detection systems often struggle with detecting novel or rare attack types, particularly when data availability is limited or heterogeneous. The current study tries to address these challenges by proposing a unified, multimodal threat detection framework that leverages the combination of synthetic data generation through Generative Adversarial Networks (GANs), advanced ensemble learning, and transfer learning techniques. The research objective is to enhance detection accuracy and resilience against zero-day, botnet, and image-based malware attacks by integrating multiple data modalities, including structured network logs and malware binaries, within a scalable and flexible pipeline. The proposed system features a dual-branch architecture: one branch uses a CNN with transfer learning for image-based malware classification, and the other employs a soft-voting ensemble classifier for tabular intrusion detection, both trained on augmented datasets generated by GANs. Experimental results demonstrate significant improvements in detection performance and false positive reduction, especially when multimodal outputs are fused using the proposed confidence-weighted strategy. The findings highlight the framework’s adaptability and practical applicability in real-world intrusion detection and response systems. Full article

Remote sensing targets typically exhibit characteristics of gradual scale changes and diverse orientations. Most existing remote sensing detectors adapt to these differences by adding multi-level structures for feature fusion. However, this approach leads to incomplete coverage of the overall target by the extracted local features, resulting in the loss of critical directional information and an increase in computational complexity which affect the detector’s performance. To address this issue, this paper proposes a Rotation Target Detection Network based on Multi-kernel Interaction and Hierarchical Expansion (MIHE-Net) as a systematic solution. Specifically, we first refine scale modeling through the Multi-kernel Context Interaction (MCI) module and Hierarchical Expansion Attention (HEA) mechanism, achieving sufficient extraction of local features and global information for targets of different scales. Additionally, the Midpoint Offset Loss Function is employed to mitigate the impact of gradual scale changes on target direction perception, enabling precise regression for targets across various scales. We conducted comparative experiments on three commonly used remote sensing target datasets (DOTA, HRSC2016, and UCAS-AOD), with mean average precision (mAP) as the core evaluation metric. The mAP values of the method in this paper on the three datasets reached 81.72%, 92.43%, and 91.86% respectively, which were 0.65%, 1.93%, and 1.87% higher than those of the optimal method, significantly outperforming existing one-stage and two-stage detectors. Through multi-scale feature interaction and direction-aware optimization, MIHE-Net effectively addresses the challenges posed by scale gradation and direction diversity in remote sensing target detection, providing an efficient and feasible solution for high-precision remote sensing target detection. Full article

Background/Objectives: Ultrasound imaging is widely employed to assess kidney health and diagnose renal diseases. Accurate segmentation of renal structures in ultrasound images plays a critical role in the diagnosis and treatment of related kidney diseases. However, challenges such as speckle noise and low contrast still hinder precise segmentation. Methods: In this work, we propose an encoder–decoder architecture, named MAT-UNet, which incorporates two distinct attention mechanisms to enhance segmentation accuracy. Specifically, the multi-convolution pixel-wise attention module utilizes the pixel-wise attention to enable the network to focus more effectively on important features at each stage. Furthermore, the triple-branch multi-head self-attention mechanism leverages the different convolution layers to obtain diverse receptive fields, capture global contextual information, compensate for the local receptive field limitations of convolution operations, and boost the segmentation performance. We evaluate the segmentation performance of the proposed MAT-UNet using the Open Kidney US Data Set (OKUD). Results: For renal capsule segmentation, MAT-UNet achieves a Dice Similarity Coefficient (DSC) of 93.83%, a 95% Hausdorff Distance (HD95) of 32.02 mm, an Average Surface Distance (ASD) of 9.80 mm, and an Intersection over Union (IOU) of 88.74%. Additionally, MAT-UNet achieves a DSC of 84.34%, HD95 of 35.79 mm, ASD of 11.17 mm, and IOU of 74.26% for central echo complex segmentation; a DSC of 66.34%, HD95 of 82.54 mm, ASD of 19.52 mm, and IOU of 51.78% for renal medulla segmentation; and a DSC of 58.93%, HD95 of 107.02 mm, ASD of 21.69 mm, and IOU of 43.61% for renal cortex segmentation. Conclusions: The experimental results demonstrate that our proposed MAT-UNet achieves superior performance in multiple renal structure segmentation in ultrasound images. Full article

Hyperspectral image classification (HSIC) is a key task in the field of remote sensing, but the complex nature of hyperspectral data poses a serious challenge to traditional methods. Although deep learning significantly improves classification performance through automatic feature extraction, manually designed network architectures suffer from issues such as dependence on expert experience and lack of flexibility. Neural architecture search (NAS) provides new ideas for HSIC through automated network structure optimization. This article systematically reviews the application progress of NAS in HSIC: firstly, the core components of NAS are analyzed, and the characteristics of various methods are compared from three aspects: search space, search strategy, and performance evaluation. Furthermore, the focus is on exploring NAS technology based on convolutional neural networks, covering 1D, 2D, and 3D convolutional architectures and their innovative integration with various technologies, revealing the advantages of NAS in HSIC. However, NAS still faces challenges such as high computing resource requirements and insufficient interpretability. This article systematically reviews the application of NAS in the field of HSIC for the first time, facilitating readers to quickly understand the development process of NAS in HSIC and the advantages and disadvantages of various technologies, proposing possible future research directions. Full article

Fibrillins (FBNs) are indispensable for plant growth and development, orchestrating multiple physiological processes. However, the precise functional role of FBNs in cotton fiber development remains uncharacterized. This study reports a genome-wide characterization of the FBN gene family in cotton. A total of 28 GhFBN genes were identified in upland cotton, with systematic analyses of their phylogenetic relationships, protein motifs, gene structures, and hormone-responsive cis-regulatory elements. Expression profiling of GhFBN1A during fiber development revealed stage-specific activity across the developmental continuum. Transcriptomic analyses following hormone treatments demonstrated upregulation of GhFBN family members, implicating their involvement in hormone-mediated regulatory networks governing fiber cell development. Collectively, this work presents a detailed molecular characterization of cotton GhFBNs and establishes a theoretical foundation for exploring their potential applications in cotton breeding programs aimed at improving fiber quality. Full article

The increasing frequency and intensity of wildfires pose serious threats to ecosystems, property, and human safety worldwide. Accurate semantic segmentation of wildfire images is essential for real-time fire monitoring, spread prediction, and disaster response. However, existing deep learning methods heavily rely on large volumes of pixel-level annotated data, which are difficult and costly to obtain in real-world wildfire scenarios due to complex environments and urgent time constraints. To address this challenge, we propose a semi-supervised wildfire image segmentation framework that enhances segmentation performance under limited annotation conditions by integrating multi-scale structural information fusion and pixel-level contrastive consistency learning. Specifically, a Lagrange Interpolation Module (LIM) is designed to construct structured interpolation representations between multi-scale feature maps during the decoding stage, enabling effective fusion of spatial details and semantic information, and improving the model’s ability to capture flame boundaries and complex textures. Meanwhile, a Pixel Contrast Consistency (PCC) mechanism is introduced to establish pixel-level semantic constraints between CutMix and Flip augmented views, guiding the model to learn consistent intra-class and discriminative inter-class feature representations, thereby reducing the reliance on large labeled datasets. Extensive experiments on two public wildfire image datasets, Flame and D-Fire, demonstrate that our method consistently outperforms other approaches under various annotation ratios. For example, with only half of the labeled data, our model achieves 5.0% and 6.4% mIoU improvements on the Flame and D-Fire datasets, respectively, compared to the baseline. This work provides technical support for efficient wildfire perception and response in practical applications. Full article

The balance between exploration and exploitation is a fundamental element in the design and performance of bio-inspired optimization algorithms. However, to date, its conceptual evolution and its treatment in the scientific literature have not been systematically characterized from a bibliometric approach. This study performs an exhaustive analysis of the scientific production on the balance between exploration and exploitation using records extracted from the Web of Science (WoS) database. The processing and analysis of the data were carried out through the combined use of Bibliometrix (R package) and VOSviewer, tools that made it possible to quantify productivity, map collaborative networks, and visualize emerging thematic trends. The results show a sustained growth in the volume of publications over the last decade, as well as the consolidation of academic collaboration networks and the emergence of new thematic lines in the field. In particular, metaheuristic algorithms have demonstrated a significant and growing impact, constituting a fundamental pillar in the advancement and methodological diversification of the exploration–exploitation balance. This work provides a quantitative framework and a structured view of the evolution of research, identifies the main actors and trends, and raises opportunities for future lines of research in the field of optimization using metaheuristics, the most prominent instantiation of bio-inspired optimization algorithms. Full article

Understanding population connectivity and genetic structure is crucial for the effective management of exploited marine species. This study investigates the population genetics of the common European squid (Loligo vulgaris) across the Mediterranean Sea, focusing on samples from the Aegean Sea and comparative sequences from Western Mediterranean, Eastern Mediterranean, and Atlantic coasts. A total of 67 COI mitochondrial sequences were analyzed, identifying 12 haplotypes and 27 polymorphic sites. Population-level genetic diversity, pairwise F_ST values, and haplotype network analyses revealed pronounced genetic differentiation in the eastern Mediterranean contrasting with the genetic homogeneity observed among Western populations. The low haplotype diversity observed in the Greek populations of L. vulgaris may be influenced by a combination of ecological and historical factors, as the Aegean region is recognized as a hotspot of endemism and historical population fragmentation. The results indicate the existence of at least two genetically differentiated clusters within the Mediterranean basin. This study advances current knowledge of the genetic structure of Loligo vulgaris by providing novel genetic data on populations from the eastern Mediterranean, offering valuable insights for future conservation and management strategies for the species. Full article

This study focuses on cultural and urban development policies under China’s 14th Five-Year Plan, exploring the content and semantic structure of discussions on the “East Asian Cultural Capital” project on the Weibo platform. It analyzes how national cultural development policies are reflected in the discourse system related to the “East Asian Cultural Capital” on social media and emphasizes the guiding role of policies in the dissemination of online culture. When China announced the 14th Five-Year Plan in 2021, the strategic direction and policy framework for cultural development over the five-year period from 2021 to 2025 were clearly outlined. This study employs text mining and semantic network analysis methods to analyze user-generated content on Weibo from 2023 to 2024, aiming to understand public perception and discourse trends. Word frequency and TF-IDF analyses identify key terms and issues, while centrality and CONCOR clustering analyses reveal the semantic structure and discourse communities. MR-QAP regression is employed to compare network changes across the two years. Findings highlight that urban cultural development, heritage preservation, and regional exchange are central themes, with digital media, cultural branding, trilateral cooperation, and cultural–economic integration emerging as key factors in regional collaboration. Full article

Quantum network correlations are crucial for long-distance quantum communication, quantum cryptography, and distributed quantum computing. Detecting network steering is particularly challenging in complex network structures. We have studied the steering inequality criteria for a 2-forked 3-layer tree-shaped network. Assuming the first and third layers are trusted and the second layer is untrusted, we derived a steering inequality criterion using the correlation matrix between trusted and untrusted observables. In particular, we apply the steering criterion to three classes of measurements which are of special significance: local orthogonal observables, mutually unbiased measurements, and general symmetric informationally complete measurements. We further illustrate the effectiveness of our method through an example. Full article

Anthracnose, a significant disease of aloe with characteristics of contact transmission, poses a considerable threat to the economic viability of aloe cultivation. To address the challenges of accurately detecting and classifying crop diseases in complex environments, this study proposes an enhanced algorithm, YOLOv11-seg-DEDB, based on the improved YOLOv11-seg model. This approach integrates multi-scale feature enhancement and a dynamic attention mechanism, aiming to achieve precise segmentation of aloe anthracnose lesions and effective disease level discrimination in complex scenarios. Specifically, a novel Disease Enhance attention mechanism is introduced, combining spatial attention and max pooling to improve the accuracy of lesion segmentation. Additionally, the DCNv2 is incorporated into the network neck to enhance the model’s ability to extract multi-scale features from targets in challenging environments. Furthermore, the Bidirectional Feature Pyramid Network structure, which includes an additional p2 detection head, replaces the original PANet network. A more lightweight detection head structure is designed, utilizing grouped convolutions and structural simplifications to reduce both the parameter count and computational load, thereby enhancing the model’s inference capability, particularly for small lesions. Experiments were conducted using a self-collected dataset of aloe anthracnose infected leaves. The results demonstrate that, compared to the original model, the improved YOLOv11-seg-DEDB model improves segmentation accuracy and mAP@50 for infected lesions by 5.3% and 3.4%, respectively. Moreover, the model size is reduced from 6.0 MB to 4.6 MB, and the number of parameters is decreased by 27.9%. YOLOv11-seg-DEDB outperforms other mainstream segmentation models, providing a more accurate solution for aloe disease segmentation and grading, thereby offering farmers and professionals more reliable disease detection outcomes. Full article

Urban public transportation systems face increasing pressure from shifting travel patterns, rising peak-hour demand, and the need for equitable and resilient service delivery. While complex network theory has been widely applied to analyze transit systems, limited attention has been paid to behavioral segmentation within such networks. This study introduces a frequency-based framework that differentiates high-frequency (HF) and low-frequency (LF) passengers to examine how distinct user groups shape network structure, congestion vulnerability, and robustness. Using over 20 million smart-card records from Beijing’s multimodal transit system, we construct and analyze directed weighted networks for HF and LF users, integrating topological metrics, temporal comparisons, and community detection. Results reveal that HF networks are densely connected but structurally fragile, exhibiting lower modularity and significantly greater efficiency loss during peak periods. In contrast, LF networks are more spatially dispersed yet resilient, maintaining stronger intracommunity stability. Peak-hour simulation shows a 70% drop in efficiency and a 99% decrease in clustering, with HF networks experiencing higher vulnerability. Based on these findings, we propose differentiated policy strategies for each user group and outline a future optimization framework constrained by budget and equity considerations. This study contributes a scalable, data-driven approach to integrating passenger behavior with network science, offering actionable insights for resilient and inclusive transit planning. Full article

Machine learning techniques, particularly decision trees, have been extensively utilized in Network-based Intrusion Detection Systems (NIDSs) due to their transparent, rule-based structures that enable straightforward interpretation. However, this transparency presents privacy risks, as decision trees may inadvertently expose sensitive information such as network configurations or IP addresses. In our previous work, we introduced a sensitive pruning-based decision tree to mitigate these risks within a limited dataset and basic pruning framework. In this extended study, three privacy-preserving pruning strategies are proposed: standard sensitive pruning, which conceals specific sensitive attribute values; optimistic sensitive pruning, which further simplifies the decision tree when the sensitive splits are minimal; and pessimistic sensitive pruning, which aggressively removes entire subtrees to maximize privacy protection. The methods are implemented using the J48 (Weka C4.5 package) decision tree algorithm and are rigorously validated across three full-scale NIDS datasets: GureKDDCup, UNSW-NB15, and CIDDS-001. To ensure a realistic evaluation of time-dependent intrusion patterns, a rolling-origin resampling scheme is employed in place of conventional cross-validation. Additionally, IP address truncation and port bilateral classification are incorporated to further enhance privacy preservation. Experimental results demonstrate that the proposed pruning strategies effectively reduce the exposure of sensitive information, significantly simplify decision tree structures, and incur only minimal reductions in classification accuracy. These findings reaffirm that privacy protection can be successfully integrated into decision tree models without severely compromising detection performance. To further support the proposed pruning strategies, this study also includes a comprehensive review of decision tree post-pruning techniques. Full article

Teledermatology relies on digital transfer of dermatological images, but compression and resolution differences compromise diagnostic quality. Image enhancement techniques are crucial to compensate for these differences and improve quality for both clinical assessment and AI-based analysis. We developed a customized image degradation pipeline simulating common artifacts in dermatological images, including blur, noise, downsampling, and compression. This synthetic degradation approach enabled effective training of DermaSR-GAN, a super-resolution generative adversarial network tailored for dermoscopic images. The model was trained on 30,000 high-quality ISIC images and evaluated on three independent datasets (ISIC Test, Novara Dermoscopic, PH²) using structural similarity and no-reference quality metrics. DermaSR-GAN achieved statistically significant improvements in quality scores across all datasets, with up to 23% enhancement in perceptual quality metrics (MANIQA). The model preserved diagnostic details while doubling resolution and surpassed existing approaches, including traditional interpolation methods and state-of-the-art deep learning techniques. Integration with downstream classification systems demonstrated up to 14.6% improvement in class-specific accuracy for keratosis-like lesions compared to original images. Synthetic degradation represents a promising approach for training effective super-resolution models in medical imaging, with significant potential for enhancing teledermatology applications and computer-aided diagnosis systems. Full article

Efficient task offloading for delay-sensitive applications, such as autonomous driving, presents a significant challenge in multi-hop Vehicular Edge Computing (VEC) networks, primarily due to high vehicle mobility, dynamic network topologies, and complex end-to-end congestion problems. To address these issues, this paper proposes a graph attention-based reinforcement learning algorithm, named GAPO. The algorithm models the dynamic VEC network as an attributed graph and utilizes a graph neural network (GNN) to learn a network state representation that captures the global topological structure and node contextual information. Building on this foundation, an attention-based Actor–Critic framework makes joint offloading decisions by intelligently selecting the optimal destination and collaboratively determining the ratios for offloading and resource allocation. A multi-objective reward function, designed to minimize task latency and to alleviate link congestion, guides the entire learning process. Comprehensive simulation experiments and ablation studies show that, compared to traditional heuristic algorithms and standard deep reinforcement learning methods, GAPO significantly reduces average task completion latency and substantially decreases backbone link congestion. In conclusion, by deeply integrating the state-aware capabilities of GNNs with the decision-making abilities of DRL, GAPO provides an efficient, adaptive, and congestion-aware solution to the resource management problems in dynamic VEC environments. Full article